Expressway vehicle detection and multi-attribute feature extraction method based on local image

Abstract

The invention provides an expressway vehicle detection and multi-attribute feature extraction method based on a local image, and relates to the technical field of intelligent transportation. A video acquisition terminal reads expressway monitoring video in real time and transmits the expressway monitoring video to an edge end, and the edge end analyzes the real-time video data by adopting a background difference method to select a key frame; a cloud end uses a VOC2007 data set and vehicle pictures collected by an expressway to train a YOLO-v3-tiny detection model, the edge end loads the trained YOLO-v3-tiny detection model to predict the position of a vehicle bounding box in the selected key frame, and then a local image of a vehicle is obtained and transmitted to the cloud end; a ResNet-50 residual neural network model is trained by the cloud end by utilizing the training set data with the multi-label type, the edge end loads the trained ResNet-50 residual neural network model, and the acquired local image of the vehicle is input into the neural network model to realize the extraction of multi-attribute features of the vehicle; and the extracted multi-attribute features of the vehicle are made into a label, and the label is uploaded to the cloud end.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for expressway vehicle detection and multi-attribute feature extraction based on partial images. Background technique [0002] In recent years, with the improvement of highway traffic conditions, the number of motor vehicles has increased sharply, which has caused difficulties in the supervision of highways. A large number of cameras are deployed at expressway checkpoints in various cities, which can generate a large amount of video data every day. Selecting key frames of the video can effectively reduce the amount of stored data. In the process of vehicle re-identification, effective vehicle detection and feature extraction methods can improve the matching degree to the target vehicle. At present, traditional feature extraction methods such as HOG feature and Haaris feature are relatively simple and are easily disturbed by the external environment. A...

AI Technical Summary

Problems solved by technology

At present, traditional feature extraction methods such as HOG features and Haaris features are relatively simple and susceptible to interference from the external environment. A single feature is not robust in vehicle recognition.

In addition, traditional video processing methods are concentrated in the cloud center, which leads to problems such as long video transmission time, large network bandwidth occupation, and heavy computing load in the cloud center.

Images